save ( save_dir = 'results/' ) Train/Detect/Test/Export show () # save results into "results/" folder results. pred boxes = predictions # x1, x2, y1, y2 scores = predictions categories = predictions # show detection bounding boxes on image results. predict (, size = 1280, augment = True ) # parse results predictions = results. predict ( image1, augment = True ) # perform inference on multiple images results = yolov5.
predict ( image1, size = 1280 ) # perform inference with test time augmentation results = yolov5. predict ( image1 ) # perform inference with larger input size results = yolov5. save ( save_dir = 'results/' ) Alternative from yolov5 import YOLOv5 # set model params model_path = "yolov5/weights/yolov5s.pt" device = "cuda:0" # or "cpu" # init yolov5 model yolov5 = YOLOv5 ( model_path, device ) # load images image1 = '' image2 = '' # perform inference results = yolov5. load ( 'yolov5s' ) # set image img = '' # perform inference results = model ( img ) # inference with larger input size results = model ( img, size = 1280 ) # inference with test time augmentation results = model ( img, augment = True ) # parse results predictions = results. Use from Python Basic import yolov5 # load model model = yolov5.
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